diff --git a/03. Machine Learning/03-02. multivariable_linear_regression.ipynb b/03. Machine Learning/03-02. multivariable_linear_regression.ipynb index a211e12..4506726 100644 --- a/03. Machine Learning/03-02. multivariable_linear_regression.ipynb +++ b/03. Machine Learning/03-02. multivariable_linear_regression.ipynb @@ -1,33 +1,17 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, "cells": [ { "cell_type": "markdown", - "source": [ - "## 2. 파이토치로 구현하기" - ], "metadata": { "id": "gcdCkILmcK4q" - } + }, + "source": [ + "## 2. 파이토치로 구현하기" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "id": "ltHD3XLrb-AB" }, @@ -41,9 +25,7 @@ }, { "cell_type": "code", - "source": [ - "torch.manual_seed(1)" - ], + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -51,76 +33,55 @@ "id": "Wpx9Z4UTcC_I", "outputId": "5910f089-06c5-4646-f2c1-82ec13589de0" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, + "execution_count": 2, "metadata": {}, - "execution_count": 2 + "output_type": "execute_result" } + ], + "source": [ + "torch.manual_seed(1)" ] }, { "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "Xw6qlsrUcDsS" + }, + "outputs": [], "source": [ "# 훈련 데이터\n", "x1_train = torch.FloatTensor([[73], [93], [89], [96], [73]])\n", "x2_train = torch.FloatTensor([[80], [88], [91], [98], [66]])\n", "x3_train = torch.FloatTensor([[75], [93], [90], [100], [70]])\n", "y_train = torch.FloatTensor([[152], [185], [180], [196], [142]])" - ], - "metadata": { - "id": "Xw6qlsrUcDsS" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "2uj4NmZQcFNw" + }, + "outputs": [], "source": [ "# 가중치 w와 편향 b 초기화\n", "w1 = torch.zeros(1, requires_grad=True)\n", "w2 = torch.zeros(1, requires_grad=True)\n", "w3 = torch.zeros(1, requires_grad=True)\n", "b = torch.zeros(1, requires_grad=True)" - ], - "metadata": { - "id": "2uj4NmZQcFNw" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "# optimizer 설정\n", - "optimizer = optim.SGD([w1, w2, w3, b], lr=1e-5)\n", - "\n", - "nb_epochs = 1000\n", - "for epoch in range(nb_epochs + 1):\n", - "\n", - " # H(x) 계산\n", - " hypothesis = x1_train * w1 + x2_train * w2 + x3_train * w3 + b\n", - "\n", - " # cost 계산\n", - " cost = torch.mean((hypothesis - y_train) ** 2)\n", - "\n", - " # cost로 H(x) 개선\n", - " optimizer.zero_grad()\n", - " cost.backward()\n", - " optimizer.step()\n", - "\n", - " # 100번마다 로그 출력\n", - " if epoch % 100 == 0:\n", - " print('Epoch {:4d}/{} w1: {:.3f} w2: {:.3f} w3: {:.3f} b: {:.3f} Cost: {:.6f}'.format(\n", - " epoch, nb_epochs, w1.item(), w2.item(), w3.item(), b.item(), cost.item()\n", - " ))" - ], + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -128,11 +89,10 @@ "id": "4PSMx9TDcGJp", "outputId": "b2de4316-4cf1-4353-d8ba-86f265399343" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 0/1000 w1: 0.294 w2: 0.294 w3: 0.297 b: 0.003 Cost: 29661.800781\n", "Epoch 100/1000 w1: 0.674 w2: 0.661 w3: 0.676 b: 0.008 Cost: 1.563628\n", @@ -147,39 +107,58 @@ "Epoch 1000/1000 w1: 0.718 w2: 0.613 w3: 0.680 b: 0.009 Cost: 1.079390\n" ] } + ], + "source": [ + "# optimizer 설정\n", + "optimizer = optim.SGD([w1, w2, w3, b], lr=1e-5)\n", + "\n", + "nb_epochs = 1000\n", + "for epoch in range(nb_epochs + 1):\n", + "\n", + " # H(x) 계산\n", + " hypothesis = x1_train * w1 + x2_train * w2 + x3_train * w3 + b\n", + "\n", + " # cost 계산\n", + " cost = torch.mean((hypothesis - y_train) ** 2)\n", + "\n", + " # cost로 H(x) 개선\n", + " optimizer.zero_grad()\n", + " cost.backward()\n", + " optimizer.step()\n", + "\n", + " # 100번마다 로그 출력\n", + " if epoch % 100 == 0:\n", + " print(f'Epoch {epoch:4d}/{nb_epochs} w1: {w1.item():.3f} w2: {w2.item():.3f} w3: {w3.item():.3f} b: {b.item():.3f} Cost: {cost.item():.6f}') " ] }, { "cell_type": "markdown", - "source": [ - "## 4. 행렬 연산을 고려하여 파이토치로 구현하기" - ], "metadata": { "id": "dab2miM6cPha" - } + }, + "source": [ + "## 4. 행렬 연산을 고려하여 파이토치로 구현하기" + ] }, { "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "GaitaK3tcHW6" + }, + "outputs": [], "source": [ "x_train = torch.FloatTensor([[73, 80, 75],\n", " [93, 88, 93],\n", - " [89, 91, 80],\n", + " [89, 91, 90],\n", " [96, 98, 100],\n", " [73, 66, 70]])\n", "y_train = torch.FloatTensor([[152], [185], [180], [196], [142]])" - ], - "metadata": { - "id": "GaitaK3tcHW6" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "print(x_train.shape)\n", - "print(y_train.shape)" - ], + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -187,48 +166,1068 @@ "id": "4QoSf6fLcQyQ", "outputId": "0540c660-787d-4237-c371-010efed0a737" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "torch.Size([5, 3])\n", "torch.Size([5, 1])\n" ] } + ], + "source": [ + "print(x_train.shape)\n", + "print(y_train.shape)" ] }, { "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "SLp7lfsvcRl4" + }, + "outputs": [], "source": [ "# 가중치와 편향 선언\n", "W = torch.zeros((3, 1), requires_grad=True)\n", "b = torch.zeros(1, requires_grad=True)" - ], - "metadata": { - "id": "SLp7lfsvcRl4" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "hypothesis = x_train.matmul(W) + b" - ], + "execution_count": 9, "metadata": { "id": "TCMDwXQjcSk6" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "hypothesis = x_train.matmul(W) + b" + ] }, { "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TSMrvUurcTeM", + "outputId": "bacadc41-ccd9-437e-f960-4f041898521e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0/1000 Weights: tensor([0.2940, 0.2936, 0.2974]) Bias: 0.003 Cost: 29661.800781\n", + "Epoch 1/1000 Weights: tensor([0.4586, 0.4579, 0.4639]) Bias: 0.005 Cost: 9298.520508\n", + "Epoch 2/1000 Weights: tensor([0.5508, 0.5499, 0.5571]) Bias: 0.006 Cost: 2915.712402\n", + "Epoch 3/1000 Weights: tensor([0.6025, 0.6014, 0.6093]) Bias: 0.007 Cost: 915.040527\n", + "Epoch 4/1000 Weights: tensor([0.6314, 0.6302, 0.6385]) Bias: 0.007 Cost: 287.936096\n", + "Epoch 5/1000 Weights: tensor([0.6476, 0.6463, 0.6549]) Bias: 0.008 Cost: 91.371063\n", + "Epoch 6/1000 Weights: tensor([0.6568, 0.6553, 0.6640]) Bias: 0.008 Cost: 29.758249\n", + "Epoch 7/1000 Weights: tensor([0.6619, 0.6603, 0.6692]) Bias: 0.008 Cost: 10.445267\n", + "Epoch 8/1000 Weights: tensor([0.6648, 0.6630, 0.6720]) Bias: 0.008 Cost: 4.391237\n", + "Epoch 9/1000 Weights: tensor([0.6664, 0.6646, 0.6737]) Bias: 0.008 Cost: 2.493121\n", + "Epoch 10/1000 Weights: tensor([0.6674, 0.6654, 0.6746]) Bias: 0.008 Cost: 1.897688\n", + "Epoch 11/1000 Weights: tensor([0.6679, 0.6658, 0.6751]) Bias: 0.008 Cost: 1.710552\n", + "Epoch 12/1000 Weights: tensor([0.6683, 0.6660, 0.6754]) Bias: 0.008 Cost: 1.651416\n", + "Epoch 13/1000 Weights: tensor([0.6685, 0.6661, 0.6755]) Bias: 0.008 Cost: 1.632369\n", + "Epoch 14/1000 Weights: tensor([0.6686, 0.6662, 0.6756]) Bias: 0.008 Cost: 1.625924\n", + "Epoch 15/1000 Weights: tensor([0.6687, 0.6661, 0.6757]) Bias: 0.008 Cost: 1.623420\n", + "Epoch 16/1000 Weights: tensor([0.6688, 0.6661, 0.6757]) Bias: 0.008 Cost: 1.622152\n", + "Epoch 17/1000 Weights: tensor([0.6689, 0.6661, 0.6757]) Bias: 0.008 Cost: 1.621262\n", + "Epoch 18/1000 Weights: tensor([0.6689, 0.6660, 0.6757]) Bias: 0.008 Cost: 1.620501\n", + "Epoch 19/1000 Weights: tensor([0.6690, 0.6660, 0.6758]) Bias: 0.008 Cost: 1.619757\n", + "Epoch 20/1000 Weights: tensor([0.6691, 0.6659, 0.6758]) Bias: 0.008 Cost: 1.619046\n", + "Epoch 21/1000 Weights: tensor([0.6691, 0.6658, 0.6758]) Bias: 0.008 Cost: 1.618348\n", + "Epoch 22/1000 Weights: tensor([0.6692, 0.6658, 0.6758]) Bias: 0.008 Cost: 1.617638\n", + "Epoch 23/1000 Weights: tensor([0.6692, 0.6657, 0.6758]) Bias: 0.008 Cost: 1.616936\n", + "Epoch 24/1000 Weights: tensor([0.6693, 0.6656, 0.6758]) Bias: 0.008 Cost: 1.616214\n", + "Epoch 25/1000 Weights: tensor([0.6693, 0.6656, 0.6758]) Bias: 0.008 Cost: 1.615513\n", + "Epoch 26/1000 Weights: tensor([0.6694, 0.6655, 0.6758]) Bias: 0.008 Cost: 1.614808\n", + "Epoch 27/1000 Weights: tensor([0.6694, 0.6655, 0.6758]) Bias: 0.008 Cost: 1.614109\n", + "Epoch 28/1000 Weights: tensor([0.6695, 0.6654, 0.6758]) Bias: 0.008 Cost: 1.613405\n", + "Epoch 29/1000 Weights: tensor([0.6696, 0.6653, 0.6758]) Bias: 0.008 Cost: 1.612695\n", + "Epoch 30/1000 Weights: tensor([0.6696, 0.6653, 0.6758]) Bias: 0.008 Cost: 1.611981\n", + "Epoch 31/1000 Weights: tensor([0.6697, 0.6652, 0.6758]) Bias: 0.008 Cost: 1.611289\n", + "Epoch 32/1000 Weights: tensor([0.6697, 0.6651, 0.6758]) Bias: 0.008 Cost: 1.610577\n", + "Epoch 33/1000 Weights: tensor([0.6698, 0.6651, 0.6758]) Bias: 0.008 Cost: 1.609871\n", + "Epoch 34/1000 Weights: tensor([0.6698, 0.6650, 0.6758]) Bias: 0.008 Cost: 1.609167\n", + "Epoch 35/1000 Weights: tensor([0.6699, 0.6650, 0.6759]) Bias: 0.008 Cost: 1.608482\n", + "Epoch 36/1000 Weights: tensor([0.6700, 0.6649, 0.6759]) Bias: 0.008 Cost: 1.607761\n", + "Epoch 37/1000 Weights: tensor([0.6700, 0.6648, 0.6759]) Bias: 0.008 Cost: 1.607080\n", + "Epoch 38/1000 Weights: tensor([0.6701, 0.6648, 0.6759]) Bias: 0.008 Cost: 1.606368\n", + "Epoch 39/1000 Weights: tensor([0.6701, 0.6647, 0.6759]) Bias: 0.008 Cost: 1.605665\n", + "Epoch 40/1000 Weights: tensor([0.6702, 0.6647, 0.6759]) Bias: 0.008 Cost: 1.604975\n", + "Epoch 41/1000 Weights: tensor([0.6702, 0.6646, 0.6759]) Bias: 0.008 Cost: 1.604280\n", + "Epoch 42/1000 Weights: tensor([0.6703, 0.6645, 0.6759]) Bias: 0.008 Cost: 1.603572\n", + "Epoch 43/1000 Weights: tensor([0.6703, 0.6645, 0.6759]) Bias: 0.008 Cost: 1.602870\n", + "Epoch 44/1000 Weights: tensor([0.6704, 0.6644, 0.6759]) Bias: 0.008 Cost: 1.602174\n", + "Epoch 45/1000 Weights: tensor([0.6705, 0.6643, 0.6759]) Bias: 0.008 Cost: 1.601473\n", + "Epoch 46/1000 Weights: tensor([0.6705, 0.6643, 0.6759]) Bias: 0.008 Cost: 1.600761\n", + "Epoch 47/1000 Weights: tensor([0.6706, 0.6642, 0.6759]) Bias: 0.008 Cost: 1.600087\n", + "Epoch 48/1000 Weights: tensor([0.6706, 0.6642, 0.6759]) Bias: 0.008 Cost: 1.599370\n", + "Epoch 49/1000 Weights: tensor([0.6707, 0.6641, 0.6759]) Bias: 0.008 Cost: 1.598685\n", + "Epoch 50/1000 Weights: tensor([0.6707, 0.6640, 0.6759]) Bias: 0.008 Cost: 1.597979\n", + "Epoch 51/1000 Weights: tensor([0.6708, 0.6640, 0.6759]) Bias: 0.008 Cost: 1.597292\n", + "Epoch 52/1000 Weights: tensor([0.6708, 0.6639, 0.6760]) Bias: 0.008 Cost: 1.596590\n", + "Epoch 53/1000 Weights: tensor([0.6709, 0.6638, 0.6760]) Bias: 0.008 Cost: 1.595898\n", + "Epoch 54/1000 Weights: tensor([0.6710, 0.6638, 0.6760]) Bias: 0.008 Cost: 1.595211\n", + "Epoch 55/1000 Weights: tensor([0.6710, 0.6637, 0.6760]) Bias: 0.008 Cost: 1.594514\n", + "Epoch 56/1000 Weights: tensor([0.6711, 0.6637, 0.6760]) Bias: 0.008 Cost: 1.593807\n", + "Epoch 57/1000 Weights: tensor([0.6711, 0.6636, 0.6760]) Bias: 0.008 Cost: 1.593116\n", + "Epoch 58/1000 Weights: tensor([0.6712, 0.6635, 0.6760]) Bias: 0.008 Cost: 1.592426\n", + "Epoch 59/1000 Weights: tensor([0.6712, 0.6635, 0.6760]) Bias: 0.008 Cost: 1.591729\n", + "Epoch 60/1000 Weights: tensor([0.6713, 0.6634, 0.6760]) Bias: 0.008 Cost: 1.591051\n", + "Epoch 61/1000 Weights: tensor([0.6713, 0.6634, 0.6760]) Bias: 0.008 Cost: 1.590355\n", + "Epoch 62/1000 Weights: tensor([0.6714, 0.6633, 0.6760]) Bias: 0.008 Cost: 1.589660\n", + "Epoch 63/1000 Weights: tensor([0.6715, 0.6632, 0.6760]) Bias: 0.008 Cost: 1.588956\n", + "Epoch 64/1000 Weights: tensor([0.6715, 0.6632, 0.6760]) Bias: 0.008 Cost: 1.588267\n", + "Epoch 65/1000 Weights: tensor([0.6716, 0.6631, 0.6760]) Bias: 0.008 Cost: 1.587576\n", + "Epoch 66/1000 Weights: tensor([0.6716, 0.6630, 0.6760]) Bias: 0.008 Cost: 1.586890\n", + "Epoch 67/1000 Weights: tensor([0.6717, 0.6630, 0.6760]) Bias: 0.008 Cost: 1.586201\n", + "Epoch 68/1000 Weights: tensor([0.6717, 0.6629, 0.6760]) Bias: 0.008 Cost: 1.585519\n", + "Epoch 69/1000 Weights: tensor([0.6718, 0.6629, 0.6761]) Bias: 0.008 Cost: 1.584820\n", + "Epoch 70/1000 Weights: tensor([0.6718, 0.6628, 0.6761]) Bias: 0.008 Cost: 1.584131\n", + "Epoch 71/1000 Weights: tensor([0.6719, 0.6627, 0.6761]) Bias: 0.008 Cost: 1.583449\n", + "Epoch 72/1000 Weights: tensor([0.6720, 0.6627, 0.6761]) Bias: 0.008 Cost: 1.582766\n", + "Epoch 73/1000 Weights: tensor([0.6720, 0.6626, 0.6761]) Bias: 0.008 Cost: 1.582072\n", + "Epoch 74/1000 Weights: tensor([0.6721, 0.6626, 0.6761]) Bias: 0.008 Cost: 1.581379\n", + "Epoch 75/1000 Weights: tensor([0.6721, 0.6625, 0.6761]) Bias: 0.008 Cost: 1.580685\n", + "Epoch 76/1000 Weights: tensor([0.6722, 0.6624, 0.6761]) Bias: 0.008 Cost: 1.580014\n", + "Epoch 77/1000 Weights: tensor([0.6722, 0.6624, 0.6761]) Bias: 0.008 Cost: 1.579320\n", + "Epoch 78/1000 Weights: tensor([0.6723, 0.6623, 0.6761]) Bias: 0.008 Cost: 1.578649\n", + "Epoch 79/1000 Weights: tensor([0.6723, 0.6623, 0.6761]) Bias: 0.008 Cost: 1.577945\n", + "Epoch 80/1000 Weights: tensor([0.6724, 0.6622, 0.6761]) Bias: 0.008 Cost: 1.577259\n", + "Epoch 81/1000 Weights: tensor([0.6725, 0.6621, 0.6761]) Bias: 0.008 Cost: 1.576571\n", + "Epoch 82/1000 Weights: tensor([0.6725, 0.6621, 0.6761]) Bias: 0.008 Cost: 1.575898\n", + "Epoch 83/1000 Weights: tensor([0.6726, 0.6620, 0.6761]) Bias: 0.008 Cost: 1.575204\n", + "Epoch 84/1000 Weights: tensor([0.6726, 0.6619, 0.6761]) Bias: 0.008 Cost: 1.574523\n", + "Epoch 85/1000 Weights: tensor([0.6727, 0.6619, 0.6761]) Bias: 0.008 Cost: 1.573825\n", + "Epoch 86/1000 Weights: tensor([0.6727, 0.6618, 0.6762]) Bias: 0.008 Cost: 1.573149\n", + "Epoch 87/1000 Weights: tensor([0.6728, 0.6618, 0.6762]) Bias: 0.008 Cost: 1.572478\n", + "Epoch 88/1000 Weights: tensor([0.6728, 0.6617, 0.6762]) Bias: 0.008 Cost: 1.571791\n", + "Epoch 89/1000 Weights: tensor([0.6729, 0.6616, 0.6762]) Bias: 0.008 Cost: 1.571114\n", + "Epoch 90/1000 Weights: tensor([0.6730, 0.6616, 0.6762]) Bias: 0.008 Cost: 1.570416\n", + "Epoch 91/1000 Weights: tensor([0.6730, 0.6615, 0.6762]) Bias: 0.008 Cost: 1.569741\n", + "Epoch 92/1000 Weights: tensor([0.6731, 0.6615, 0.6762]) Bias: 0.008 Cost: 1.569064\n", + "Epoch 93/1000 Weights: tensor([0.6731, 0.6614, 0.6762]) Bias: 0.008 Cost: 1.568379\n", + "Epoch 94/1000 Weights: tensor([0.6732, 0.6613, 0.6762]) Bias: 0.008 Cost: 1.567695\n", + "Epoch 95/1000 Weights: tensor([0.6732, 0.6613, 0.6762]) Bias: 0.008 Cost: 1.567017\n", + "Epoch 96/1000 Weights: tensor([0.6733, 0.6612, 0.6762]) Bias: 0.008 Cost: 1.566340\n", + "Epoch 97/1000 Weights: tensor([0.6733, 0.6612, 0.6762]) Bias: 0.008 Cost: 1.565659\n", + "Epoch 98/1000 Weights: tensor([0.6734, 0.6611, 0.6762]) Bias: 0.008 Cost: 1.564988\n", + "Epoch 99/1000 Weights: tensor([0.6735, 0.6610, 0.6762]) Bias: 0.008 Cost: 1.564298\n", + "Epoch 100/1000 Weights: tensor([0.6735, 0.6610, 0.6762]) Bias: 0.008 Cost: 1.563628\n", + "Epoch 101/1000 Weights: tensor([0.6736, 0.6609, 0.6762]) Bias: 0.008 Cost: 1.562946\n", + "Epoch 102/1000 Weights: tensor([0.6736, 0.6608, 0.6762]) Bias: 0.008 Cost: 1.562267\n", + "Epoch 103/1000 Weights: tensor([0.6737, 0.6608, 0.6762]) Bias: 0.008 Cost: 1.561608\n", + "Epoch 104/1000 Weights: tensor([0.6737, 0.6607, 0.6763]) Bias: 0.008 Cost: 1.560913\n", + "Epoch 105/1000 Weights: tensor([0.6738, 0.6607, 0.6763]) Bias: 0.008 Cost: 1.560245\n", + "Epoch 106/1000 Weights: tensor([0.6738, 0.6606, 0.6763]) Bias: 0.008 Cost: 1.559557\n", + "Epoch 107/1000 Weights: tensor([0.6739, 0.6605, 0.6763]) Bias: 0.008 Cost: 1.558894\n", + "Epoch 108/1000 Weights: tensor([0.6739, 0.6605, 0.6763]) Bias: 0.008 Cost: 1.558212\n", + "Epoch 109/1000 Weights: tensor([0.6740, 0.6604, 0.6763]) Bias: 0.008 Cost: 1.557544\n", + "Epoch 110/1000 Weights: tensor([0.6741, 0.6604, 0.6763]) Bias: 0.008 Cost: 1.556856\n", + "Epoch 111/1000 Weights: tensor([0.6741, 0.6603, 0.6763]) Bias: 0.008 Cost: 1.556194\n", + "Epoch 112/1000 Weights: tensor([0.6742, 0.6602, 0.6763]) Bias: 0.008 Cost: 1.555524\n", + "Epoch 113/1000 Weights: tensor([0.6742, 0.6602, 0.6763]) Bias: 0.008 Cost: 1.554850\n", + "Epoch 114/1000 Weights: tensor([0.6743, 0.6601, 0.6763]) Bias: 0.008 Cost: 1.554168\n", + "Epoch 115/1000 Weights: tensor([0.6743, 0.6601, 0.6763]) Bias: 0.008 Cost: 1.553498\n", + "Epoch 116/1000 Weights: tensor([0.6744, 0.6600, 0.6763]) Bias: 0.008 Cost: 1.552829\n", + "Epoch 117/1000 Weights: tensor([0.6744, 0.6599, 0.6763]) Bias: 0.008 Cost: 1.552154\n", + "Epoch 118/1000 Weights: tensor([0.6745, 0.6599, 0.6763]) Bias: 0.008 Cost: 1.551479\n", + "Epoch 119/1000 Weights: tensor([0.6746, 0.6598, 0.6763]) Bias: 0.008 Cost: 1.550800\n", + "Epoch 120/1000 Weights: 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tensor([0.7145, 0.6162, 0.6799]) Bias: 0.009 Cost: 1.112311\n", + "Epoch 924/1000 Weights: tensor([0.7145, 0.6162, 0.6799]) Bias: 0.009 Cost: 1.111870\n", + "Epoch 925/1000 Weights: tensor([0.7146, 0.6161, 0.6799]) Bias: 0.009 Cost: 1.111439\n", + "Epoch 926/1000 Weights: tensor([0.7146, 0.6161, 0.6799]) Bias: 0.009 Cost: 1.111012\n", + "Epoch 927/1000 Weights: tensor([0.7147, 0.6160, 0.6799]) Bias: 0.009 Cost: 1.110575\n", + "Epoch 928/1000 Weights: tensor([0.7147, 0.6160, 0.6799]) Bias: 0.009 Cost: 1.110134\n", + "Epoch 929/1000 Weights: tensor([0.7148, 0.6159, 0.6799]) Bias: 0.009 Cost: 1.109696\n", + "Epoch 930/1000 Weights: tensor([0.7148, 0.6159, 0.6799]) Bias: 0.009 Cost: 1.109268\n", + "Epoch 931/1000 Weights: tensor([0.7149, 0.6158, 0.6799]) Bias: 0.009 Cost: 1.108825\n", + "Epoch 932/1000 Weights: tensor([0.7149, 0.6158, 0.6799]) Bias: 0.009 Cost: 1.108399\n", + "Epoch 933/1000 Weights: tensor([0.7149, 0.6157, 0.6799]) Bias: 0.009 Cost: 1.107967\n", + "Epoch 934/1000 Weights: tensor([0.7150, 0.6157, 0.6799]) Bias: 0.009 Cost: 1.107539\n", + "Epoch 935/1000 Weights: tensor([0.7150, 0.6156, 0.6799]) Bias: 0.009 Cost: 1.107096\n", + "Epoch 936/1000 Weights: tensor([0.7151, 0.6156, 0.6799]) Bias: 0.009 Cost: 1.106661\n", + "Epoch 937/1000 Weights: tensor([0.7151, 0.6155, 0.6799]) Bias: 0.009 Cost: 1.106241\n", + "Epoch 938/1000 Weights: tensor([0.7152, 0.6155, 0.6799]) Bias: 0.009 Cost: 1.105795\n", + "Epoch 939/1000 Weights: tensor([0.7152, 0.6154, 0.6799]) Bias: 0.009 Cost: 1.105364\n", + "Epoch 940/1000 Weights: tensor([0.7153, 0.6154, 0.6799]) Bias: 0.009 Cost: 1.104925\n", + "Epoch 941/1000 Weights: tensor([0.7153, 0.6154, 0.6799]) Bias: 0.009 Cost: 1.104497\n", + "Epoch 942/1000 Weights: tensor([0.7153, 0.6153, 0.6800]) Bias: 0.009 Cost: 1.104059\n", + "Epoch 943/1000 Weights: tensor([0.7154, 0.6153, 0.6800]) Bias: 0.009 Cost: 1.103629\n", + "Epoch 944/1000 Weights: tensor([0.7154, 0.6152, 0.6800]) Bias: 0.009 Cost: 1.103204\n", + "Epoch 945/1000 Weights: tensor([0.7155, 0.6152, 0.6800]) Bias: 0.009 Cost: 1.102759\n", + "Epoch 946/1000 Weights: tensor([0.7155, 0.6151, 0.6800]) Bias: 0.009 Cost: 1.102327\n", + "Epoch 947/1000 Weights: tensor([0.7156, 0.6151, 0.6800]) Bias: 0.009 Cost: 1.101910\n", + "Epoch 948/1000 Weights: tensor([0.7156, 0.6150, 0.6800]) Bias: 0.009 Cost: 1.101483\n", + "Epoch 949/1000 Weights: tensor([0.7157, 0.6150, 0.6800]) Bias: 0.009 Cost: 1.101051\n", + "Epoch 950/1000 Weights: tensor([0.7157, 0.6149, 0.6800]) Bias: 0.009 Cost: 1.100610\n", + "Epoch 951/1000 Weights: tensor([0.7158, 0.6149, 0.6800]) Bias: 0.009 Cost: 1.100186\n", + "Epoch 952/1000 Weights: tensor([0.7158, 0.6148, 0.6800]) Bias: 0.009 Cost: 1.099754\n", + "Epoch 953/1000 Weights: tensor([0.7158, 0.6148, 0.6800]) Bias: 0.009 Cost: 1.099326\n", + "Epoch 954/1000 Weights: tensor([0.7159, 0.6147, 0.6800]) Bias: 0.009 Cost: 1.098895\n", + "Epoch 955/1000 Weights: tensor([0.7159, 0.6147, 0.6800]) Bias: 0.009 Cost: 1.098463\n", + "Epoch 956/1000 Weights: tensor([0.7160, 0.6146, 0.6800]) Bias: 0.009 Cost: 1.098038\n", + "Epoch 957/1000 Weights: tensor([0.7160, 0.6146, 0.6800]) Bias: 0.009 Cost: 1.097600\n", + "Epoch 958/1000 Weights: tensor([0.7161, 0.6145, 0.6800]) Bias: 0.009 Cost: 1.097185\n", + "Epoch 959/1000 Weights: tensor([0.7161, 0.6145, 0.6800]) Bias: 0.009 Cost: 1.096748\n", + "Epoch 960/1000 Weights: tensor([0.7162, 0.6144, 0.6800]) Bias: 0.009 Cost: 1.096329\n", + "Epoch 961/1000 Weights: tensor([0.7162, 0.6144, 0.6800]) Bias: 0.009 Cost: 1.095897\n", + "Epoch 962/1000 Weights: tensor([0.7162, 0.6143, 0.6800]) Bias: 0.009 Cost: 1.095461\n", + "Epoch 963/1000 Weights: tensor([0.7163, 0.6143, 0.6800]) Bias: 0.009 Cost: 1.095040\n", + "Epoch 964/1000 Weights: tensor([0.7163, 0.6142, 0.6800]) Bias: 0.009 Cost: 1.094604\n", + "Epoch 965/1000 Weights: tensor([0.7164, 0.6142, 0.6800]) Bias: 0.009 Cost: 1.094183\n", + "Epoch 966/1000 Weights: tensor([0.7164, 0.6142, 0.6800]) Bias: 0.009 Cost: 1.093765\n", + "Epoch 967/1000 Weights: tensor([0.7165, 0.6141, 0.6800]) Bias: 0.009 Cost: 1.093323\n", + "Epoch 968/1000 Weights: tensor([0.7165, 0.6141, 0.6800]) Bias: 0.009 Cost: 1.092909\n", + "Epoch 969/1000 Weights: tensor([0.7166, 0.6140, 0.6800]) Bias: 0.009 Cost: 1.092482\n", + "Epoch 970/1000 Weights: tensor([0.7166, 0.6140, 0.6800]) Bias: 0.009 Cost: 1.092059\n", + "Epoch 971/1000 Weights: tensor([0.7166, 0.6139, 0.6800]) Bias: 0.009 Cost: 1.091629\n", + "Epoch 972/1000 Weights: tensor([0.7167, 0.6139, 0.6800]) Bias: 0.009 Cost: 1.091201\n", + "Epoch 973/1000 Weights: tensor([0.7167, 0.6138, 0.6801]) Bias: 0.009 Cost: 1.090782\n", + "Epoch 974/1000 Weights: tensor([0.7168, 0.6138, 0.6801]) Bias: 0.009 Cost: 1.090344\n", + "Epoch 975/1000 Weights: tensor([0.7168, 0.6137, 0.6801]) Bias: 0.009 Cost: 1.089915\n", + "Epoch 976/1000 Weights: tensor([0.7169, 0.6137, 0.6801]) Bias: 0.009 Cost: 1.089501\n", + "Epoch 977/1000 Weights: tensor([0.7169, 0.6136, 0.6801]) Bias: 0.009 Cost: 1.089078\n", + "Epoch 978/1000 Weights: tensor([0.7170, 0.6136, 0.6801]) Bias: 0.009 Cost: 1.088650\n", + "Epoch 979/1000 Weights: tensor([0.7170, 0.6135, 0.6801]) Bias: 0.009 Cost: 1.088228\n", + "Epoch 980/1000 Weights: tensor([0.7170, 0.6135, 0.6801]) Bias: 0.009 Cost: 1.087810\n", + "Epoch 981/1000 Weights: tensor([0.7171, 0.6134, 0.6801]) Bias: 0.009 Cost: 1.087382\n", + "Epoch 982/1000 Weights: tensor([0.7171, 0.6134, 0.6801]) Bias: 0.009 Cost: 1.086974\n", + "Epoch 983/1000 Weights: tensor([0.7172, 0.6133, 0.6801]) Bias: 0.009 Cost: 1.086538\n", + "Epoch 984/1000 Weights: tensor([0.7172, 0.6133, 0.6801]) Bias: 0.009 Cost: 1.086118\n", + "Epoch 985/1000 Weights: tensor([0.7173, 0.6132, 0.6801]) Bias: 0.009 Cost: 1.085680\n", + "Epoch 986/1000 Weights: tensor([0.7173, 0.6132, 0.6801]) Bias: 0.009 Cost: 1.085268\n", + "Epoch 987/1000 Weights: tensor([0.7173, 0.6132, 0.6801]) Bias: 0.009 Cost: 1.084856\n", + "Epoch 988/1000 Weights: tensor([0.7174, 0.6131, 0.6801]) Bias: 0.009 Cost: 1.084422\n", + "Epoch 989/1000 Weights: tensor([0.7174, 0.6131, 0.6801]) Bias: 0.009 Cost: 1.084002\n", + "Epoch 990/1000 Weights: tensor([0.7175, 0.6130, 0.6801]) Bias: 0.009 Cost: 1.083584\n", + "Epoch 991/1000 Weights: tensor([0.7175, 0.6130, 0.6801]) Bias: 0.009 Cost: 1.083162\n", + "Epoch 992/1000 Weights: tensor([0.7176, 0.6129, 0.6801]) Bias: 0.009 Cost: 1.082736\n", + "Epoch 993/1000 Weights: tensor([0.7176, 0.6129, 0.6801]) Bias: 0.009 Cost: 1.082330\n", + "Epoch 994/1000 Weights: tensor([0.7177, 0.6128, 0.6801]) Bias: 0.009 Cost: 1.081900\n", + "Epoch 995/1000 Weights: tensor([0.7177, 0.6128, 0.6801]) Bias: 0.009 Cost: 1.081479\n", + "Epoch 996/1000 Weights: tensor([0.7177, 0.6127, 0.6801]) Bias: 0.009 Cost: 1.081054\n", + "Epoch 997/1000 Weights: tensor([0.7178, 0.6127, 0.6801]) Bias: 0.009 Cost: 1.080639\n", + "Epoch 998/1000 Weights: tensor([0.7178, 0.6126, 0.6801]) Bias: 0.009 Cost: 1.080222\n", + "Epoch 999/1000 Weights: tensor([0.7179, 0.6126, 0.6801]) Bias: 0.009 Cost: 1.079796\n", + "Epoch 1000/1000 Weights: tensor([0.7179, 0.6125, 0.6801]) Bias: 0.009 Cost: 1.079390\n" + ] + } + ], "source": [ "x_train = torch.FloatTensor([[73, 80, 75],\n", " [93, 88, 93],\n", - " [89, 91, 80],\n", + " [89, 91, 90],\n", " [96, 98, 100],\n", " [73, 66, 70]])\n", "y_train = torch.FloatTensor([[152], [185], [180], [196], [142]])\n", @@ -239,7 +1238,7 @@ "# optimizer 설정\n", "optimizer = optim.SGD([W, b], lr=1e-5)\n", "\n", - "nb_epochs = 20\n", + "nb_epochs = 1000\n", "for epoch in range(nb_epochs + 1):\n", "\n", " # H(x) 계산\n", @@ -254,74 +1253,51 @@ " cost.backward()\n", " optimizer.step()\n", "\n", - " print('Epoch {:4d}/{} hypothesis: {} Cost: {:.6f}'.format(\n", - " epoch, nb_epochs, hypothesis.squeeze().detach(), cost.item()\n", - " ))" - ], + " print(f'Epoch {epoch:4d}/{nb_epochs} Weights: {W.squeeze().detach()} Bias: {b.item():.3f} Cost: {cost.item():.6f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "TSMrvUurcTeM", - "outputId": "bacadc41-ccd9-437e-f960-4f041898521e" + "id": "L8VBSlwLcoFq", + "outputId": "7b2c32b7-02d2-4076-ab3c-dddf9fafece6" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "Epoch 0/20 hypothesis: tensor([0., 0., 0., 0., 0.]) Cost: 29661.800781\n", - "Epoch 1/20 hypothesis: tensor([66.7178, 80.1701, 76.1025, 86.0194, 61.1565]) Cost: 9537.694336\n", - "Epoch 2/20 hypothesis: tensor([104.5421, 125.6208, 119.2478, 134.7862, 95.8280]) Cost: 3069.590088\n", - "Epoch 3/20 hypothesis: tensor([125.9858, 151.3882, 143.7087, 162.4333, 115.4844]) Cost: 990.670288\n", - "Epoch 4/20 hypothesis: tensor([138.1429, 165.9963, 157.5768, 178.1071, 126.6283]) Cost: 322.481873\n", - "Epoch 5/20 hypothesis: tensor([145.0350, 174.2780, 165.4395, 186.9928, 132.9461]) Cost: 107.717064\n", - "Epoch 6/20 hypothesis: tensor([148.9423, 178.9730, 169.8976, 192.0301, 136.5279]) Cost: 38.687496\n", - "Epoch 7/20 hypothesis: tensor([151.1574, 181.6346, 172.4254, 194.8856, 138.5585]) Cost: 16.499043\n", - "Epoch 8/20 hypothesis: tensor([152.4131, 183.1435, 173.8590, 196.5043, 139.7097]) Cost: 9.365656\n", - "Epoch 9/20 hypothesis: tensor([153.1250, 183.9988, 174.6723, 197.4217, 140.3625]) Cost: 7.071114\n", - "Epoch 10/20 hypothesis: tensor([153.5285, 184.4835, 175.1338, 197.9415, 140.7325]) Cost: 6.331847\n", - "Epoch 11/20 hypothesis: tensor([153.7572, 184.7582, 175.3958, 198.2360, 140.9424]) Cost: 6.092532\n", - "Epoch 12/20 hypothesis: tensor([153.8868, 184.9138, 175.5449, 198.4026, 141.0613]) Cost: 6.013817\n", - "Epoch 13/20 hypothesis: tensor([153.9602, 185.0019, 175.6299, 198.4969, 141.1288]) Cost: 5.986785\n", - "Epoch 14/20 hypothesis: tensor([154.0017, 185.0517, 175.6785, 198.5500, 141.1671]) Cost: 5.976325\n", - "Epoch 15/20 hypothesis: tensor([154.0252, 185.0798, 175.7065, 198.5800, 141.1888]) Cost: 5.971208\n", - "Epoch 16/20 hypothesis: tensor([154.0385, 185.0956, 175.7229, 198.5966, 141.2012]) Cost: 5.967835\n", - "Epoch 17/20 hypothesis: tensor([154.0459, 185.1045, 175.7326, 198.6059, 141.2082]) Cost: 5.964969\n", - "Epoch 18/20 hypothesis: tensor([154.0501, 185.1094, 175.7386, 198.6108, 141.2122]) Cost: 5.962291\n", - "Epoch 19/20 hypothesis: tensor([154.0524, 185.1120, 175.7424, 198.6134, 141.2145]) Cost: 5.959664\n", - "Epoch 20/20 hypothesis: tensor([154.0536, 185.1134, 175.7451, 198.6145, 141.2158]) Cost: 5.957089\n" + "Predicted value for input [75.0, 85.0, 72.0]: 154.88900756835938\n" ] } - ] - }, - { - "cell_type": "code", + ], "source": [ "# 임의의 입력 값에 대한 예측\n", "with torch.no_grad():\n", " new_input = torch.FloatTensor([[75, 85, 72]]) # 예측하고 싶은 임의의 입력\n", " prediction = new_input.matmul(W) + b\n", " print('Predicted value for input {}: {}'.format(new_input.squeeze().tolist(), prediction.item()))" - ], - "metadata": { - "id": "L8VBSlwLcoFq", - "outputId": "7b2c32b7-02d2-4076-ab3c-dddf9fafece6", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Predicted value for input [75.0, 85.0, 72.0]: 156.8051300048828\n" - ] - } ] } - ] -} \ No newline at end of file + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/03. Machine Learning/03-02. multivariable_linear_regression.py b/03. Machine Learning/03-02. multivariable_linear_regression.py index 433314d..58901ac 100644 --- a/03. Machine Learning/03-02. multivariable_linear_regression.py +++ b/03. Machine Learning/03-02. multivariable_linear_regression.py @@ -55,7 +55,7 @@ x_train = torch.FloatTensor([[73, 80, 75], [93, 88, 93], - [89, 91, 80], + [89, 91, 90], [96, 98, 100], [73, 66, 70]]) y_train = torch.FloatTensor([[152], [185], [180], [196], [142]]) @@ -71,7 +71,7 @@ x_train = torch.FloatTensor([[73, 80, 75], [93, 88, 93], - [89, 91, 80], + [89, 91, 90], [96, 98, 100], [73, 66, 70]]) y_train = torch.FloatTensor([[152], [185], [180], [196], [142]]) @@ -97,8 +97,8 @@ cost.backward() optimizer.step() - print('Epoch {:4d}/{} hypothesis: {} Cost: {:.6f}'.format( - epoch, nb_epochs, hypothesis.squeeze().detach(), cost.item() + print('Epoch {:4d}/{} Weights: {} Bias: {} Cost: {:.6f}'.format( + epoch, nb_epochs, W.squeeze().detach(), b.item(), cost.item() )) # 임의의 입력 값에 대한 예측